Long only 1/n portfolio#
import pandas as pd
pd.options.plotting.backend = "plotly"
import yfinance as yf
from cvx.simulator.builder import builder
from cvx.simulator.grid import resample_index
data = yf.download(tickers = "SPY AAPL GOOG MSFT", # list of tickers
period = "10y", # time period
interval = "1d", # trading interval
prepost = False, # download pre/post market hours data?
repair = True) # repair obvious price errors e.g. 100x?
[ 0% ]
[**********************50% ] 2 of 4 completed
[**********************75%*********** ] 3 of 4 completed
[*********************100%***********************] 4 of 4 completed
prices = data["Adj Close"]
capital = 1e6
b = builder(prices=prices, initial_cash=capital)
for time, state in b:
# each day we invest a quarter of the capital in the assets
b[time[-1]] = 0.25 * state.nav / state.prices
portfolio = b.build()
portfolio.profit.cumsum().plot()
portfolio.nav.plot()
Rebalancing#
Usually we would not execute on a daily basis but rather rebalance every week, month or quarter. There are two approaches to deal with this problem in cvxsimulator.
Resample the existing daily portfolio (helpful to see effect of your hesitated trading)
Trade only on days that are within a predefined grid (most flexible if you have a rather irregular grid)
Resample an existing portfolio#
portfolio_resampled = portfolio.resample(rule="M")
frame = pd.DataFrame({"original": portfolio.nav, "monthly": portfolio_resampled.nav})
frame
| original | monthly | |
|---|---|---|
| Date | ||
| 2013-06-26 | 1.000000e+06 | 1.000000e+06 |
| 2013-06-27 | 1.001717e+06 | 1.001717e+06 |
| 2013-06-28 | 1.002803e+06 | 1.002776e+06 |
| 2013-07-01 | 1.013127e+06 | 1.013052e+06 |
| 2013-07-02 | 1.013945e+06 | 1.013869e+06 |
| ... | ... | ... |
| 2023-06-20 | 8.252087e+06 | 8.233505e+06 |
| 2023-06-21 | 8.159265e+06 | 8.141517e+06 |
| 2023-06-22 | 8.281850e+06 | 8.263048e+06 |
| 2023-06-23 | 8.219862e+06 | 8.201233e+06 |
| 2023-06-26 | 8.134537e+06 | 8.117117e+06 |
2517 rows × 2 columns
print(portfolio_resampled.stocks)
AAPL GOOG MSFT SPY
Date
2013-06-26 20254.443041 11489.134619 8733.808933 1875.541820
2013-06-27 20254.443041 11489.134619 8733.808933 1875.541820
2013-06-28 20254.443041 11489.134619 8733.808933 1875.541820
2013-07-01 19961.210748 11453.404026 8845.887575 1885.796974
2013-07-02 19961.210748 11453.404026 8845.887575 1885.796974
... ... ... ... ...
2023-06-20 11229.723206 16260.840769 6080.825343 4812.178297
2023-06-21 11229.723206 16260.840769 6080.825343 4812.178297
2023-06-22 11229.723206 16260.840769 6080.825343 4812.178297
2023-06-23 11229.723206 16260.840769 6080.825343 4812.178297
2023-06-26 11229.723206 16260.840769 6080.825343 4812.178297
[2517 rows x 4 columns]
# almost hard to see that difference between the original and resampled portfolio
frame.plot()
# number of shares traded
portfolio_resampled.trades_stocks.iloc[1:].plot()
Trade only days in predefined grid#
b = builder(prices=prices, initial_cash=capital)
# define a grid
grid = resample_index(prices.index, rule="M")
for time, state in b:
# each day we invest a quarter of the capital in the assets
if time[-1] in grid:
b[time[-1]] = 0.25 * state.nav / state.prices
else:
# forward fill an existing position
b[time[-1]] = b[time[-2]]
portfolio = b.build()
portfolio.nav.plot()
# Trading only once a month can lead to days where 150k had to be reallocated
portfolio.turnover.iloc[1:].plot()
Why not resampling the prices?#
I don’t believe in bringing the prices to a monthly grid. This would render it hard to construct signals given the sparse grid. We stay on a daily grid and trade once a month.